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Using Data Analytics To Detect Fraud

Using Data Analytics To Detect Fraud. 94th Annual Professional Conference – Engaging Your New World. Session Agenda. Utilizing data analysis to detect fraud and strengthen internal controls. Risk assessment process How data analysis can be used in detecting fraud

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Using Data Analytics To Detect Fraud

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  1. UsingData Analytics To Detect Fraud 94th Annual Professional Conference – Engaging Your New World

  2. Session Agenda Utilizing data analysis to detect fraud and strengthen internal controls. • Risk assessment process • How data analysis can be used in detecting fraud • Fine-tuning a successful data analytics program • The continuous monitoring process

  3. Definition of Fraud • Webster’s Dictionary: “Deceit, trickery; cheating, intentional deception to cause a person to give up property or some lawful right.” • AICPA EDP Fraud Review Task Force: “Any intentional act, or series of acts, that is designed to deceive or mislead others and that has an impact or potential impact on an organization’s financial statements.” • The Accountant’s Handbook of Fraud & Commercial Crime: “Fraud is criminal deceptionintended to financially benefit the deceiver.”

  4. Fraud Triangle RATIONALIZATION OPPORTUNITY PRESSURE

  5. DETECTION PREVENTION INVESTIGATION Fraud Triangle OPPORTUNITY RATIONALIZATION PRESSURE

  6. Fraud Risk Universe External Board

  7. Evolution of Fraud 1950 2000 Matrix organizations Automation Autonomous authority Multiple vendors Performance-based pay • Straight-line reporting • Manual processes • Single suppliers • Step-up salary structure

  8. Spotting Potential Risks - People

  9. Should Fraud Be A Concern? • Of 4,000 high school students with A and B averages, 75 percent admit to cheating to get ahead. Ninety- two percent of those who said they cheated were never caught. - Who’s Who Among American High School Students • Almost 80 percent of college students admit to cheating at least once. - The Center for Academic Integrity • The percentage of resumes and job applications that contain lies and exaggerations has been estimated between 30 and 80 percent. - Security Management Magazine

  10. A Study of White Collar Crime Profiles 10 % of the people will never steal 10 % of the people will steal regardless of the circumstances 80 % of the people will steal if given the right motive or pressure

  11. Behavioral Red Flags of Perpetrators Source: 2010 ACFE Report To The Nation

  12. Primary Internal Control Weakness Observed Source: ACFE 2010 Report To The Nation

  13. The Whistleblower

  14. Benefits of Using Data Analytics • Efficiency • Increased analytical capacity • Variety of output representation • Repeatable process

  15. Data – Excellent Source of Fraud Evidence • Data is objective. • Data can be searched and analyzed without arousing suspicion. • Data analysis can provide evidence that helps with other areas of the investigation. • Provide facts needed to gain confessions during interviews.

  16. Data Mining Best Practices • Establish expectations: • What do I expect to see in the data • What do I expect not to see in the data • What do I expect an exception to look like • What exceptions should I expect

  17. Steps to Detect Fraud • Understand the Industry, Business, Unit • What Fraud Schemes Could Occur • Identify the Red Flags Associated With The Fraud Scheme • Test For The Red Flags • Resolve Identified Red Flags

  18. A Series of Examples – Total Disbursements Sort the total payments made to each vendor in descending order. Vendor NameDisbursement To Date Fabric Distributors Inc. $1,873,980 Silk Designer Patterns Inc. $1,792,621 Men’s Apparel Option Inc. $1,021,426 Timothy Wineguard $1,004,372 Threads Unlimited LLC $ 981,982 Wool Makers Incorporated $ 942,533

  19. A Series of Examples – Check Disbursements Vendor IDVendor NameDisbursement To Date 362862 TAILOR MADE SOLUTIONS $32,975 382868 TAILOR MAIDE SOLUTIONS $35,583 373920 TAILOR MADE SOLUITIONS $59,012

  20. A Series of Examples – Disbursements by Vendor Know your Alphabet Vendors Vendor Check DateCheck Amount AT&T 01/03/09 $1,493.43 AT&T 02/02/09 $1,394.99 AT&T 02/05/09 $2,049.63 AT&T 02/23/09 $1,032.88 AT&T 03/02/09 $1,382.21

  21. A Series of Examples – Disbursements by Frequency Determine if a Vendor is receiving payments more frequently than expected. Vendor# of Payments in 12 months Fabric Distributors Inc. 11 Silk Designer Patterns Inc. 10 Men’s Apparel Option Inc. 11 Timothy Wineguard 43 Threads Unlimited LLC 10 Wool Makers Incorporated 11

  22. A Series of Examples – Duplicates Identify common disbursement amounts, invoice numbers. VendorAmountDateInvoice TAILOR MADE SOLUTIONS $83,298.23 10/02/09 CO9291 TAILOR MAIDE SOLUTIONS $85,001.22 11/02/09 C09293 TAILOR MADE SOLUTIONS $85,001.22 11/02/09 CO9293 TAILOR MADE SOLUTIONS $83,442.35 12/02/09 CO9360

  23. A Series of Examples – Invoices Ck. DatePayeeInvoice #Ck #Ck Amount 03/12/09 Fabrics Ltd. 203920 2044 $112,400 03/20/09 Fabrics Ltd. 203921 2053 $112,400 03/31/09 Fabrics Ltd. 203922 2072 $112,500 04/05/09 Fabrics Ltd. 203023 2187 $152,839 04/05/09 Fabrics Ltd. 203025 2187 $153,839 Test for shell companies based on invoice numbers issued.

  24. Searching For Addresses Must be consistent in entering data for both the Employee Master and the Vendor Master • St. vs. Street • NE vs. North East • 123 Main Street, Apartment B vs. • 123 Main Street B • 123 Main Street Apartment B

  25. Geo-Coding • You entered: 1089 East Harrison Street, Martinsville, IN 46151 The Google geocoder found: 1089 E Harrison St, Martinsville, IN 46151, USA street address: 1089 E Harrison St ZIP/postal code: 46151 city: Martinsville county/district: Morgan state/province: IN country: USA latitude, longitude: 39.429443, -86.415746 • 39.429443 -86.415746 • N39° 25.7666', W086° 24.9448' • (precision: address) Source: http://www.gpsvisualizer.com/geocode

  26. CASE STUDY 1 • Vice President’s use of Credit Card • Confidential – do not open • Mischaracterized expenses • Duplicate receipts • Discovered – Final Bill after taking employment elsewhere.

  27. CASE STUDY 2 • Accounts Receivable • Exception to normal registration • Credit Card payments • Discovered – Bank procedures

  28. CASE STUDY 3 • Department Head • Purchases delivered to home • Asset hard to value • Discovered – Asset Audit when DH left employment

  29. THE VISION Assess where you are today, define where you want to be and begin to close the gap. Repeatable Analysis RISK ROI Ad Hoc Analysis Raw Data – System Reports INTELLIGENCE INFORMATION DISPARATE DATA

  30. QUESTIONS

  31. For more information, contact: Brenda Buetow Direct 317.269.6697 Mobile 765.318.0919 Brenda.Buetow@crowehorwath.com

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